Battery State of Health Prediction System
Advanced AI platform for predicting EV battery State of Health using manually supplied battery parameters and intelligent degradation analysis.
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Manual Input
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Feature Extract
memory
DL Model
analytics
SOH Result
Model Accuracy
99.2%
trending_up MAE: 0.0042
Dataset Size
45.2k
Validated lab samples
Total Runs
1,842
Successful inferences
Model Status
Active
Transformer-v4.2
Avg. Inference
12ms
Real-time latency
Last Run
Oct 24
14:22 UTC
biotech
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Manual Battery Input
Specify electrochemical parameters for neural analysis
Acceptable range: 3.4V - 3.7V
Acceptable range: 20°C - 45°C
Acceptable range: 1.5Ah - 2.2Ah
Acceptable range: 1 - 167 cycles
Last Model Result
89.4%
ESTIMATED SOH
Confidence
99.2%
Category
OPTIMAL
RUL
0 cycles
Est. Cycles Left
0
Degradation Severity
Low
Degradation Analysis Output
Predicted vs Empirical SOH Curve
0 Cycles
500 Cycles
1000 Cycles
1500 Cycles
2000 Cycles
NASA Dataset Module
NASA Dataset Statistics
Total Records:
1,415
Battery ID:
B0005
Mean SOH:
98.2%
Std Dev:
1.2%
Features:
8 Primary
Model Information
Architecture:
LSTM
Training Data:
NASA
Status:
Trained